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» Feature Selection via Mathematical Programming
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POPL
2010
ACM
15 years 7 months ago
A Theory of Indirection via Approximation
Building semantic models that account for various kinds of indirect reference has traditionally been a difficult problem. Indirect reference can appear in many guises, such as hea...
Aquinas Hobor, Robert Dockins, Andrew W. Appel
ML
2002
ACM
167views Machine Learning» more  ML 2002»
14 years 9 months ago
Linear Programming Boosting via Column Generation
We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as...
Ayhan Demiriz, Kristin P. Bennett, John Shawe-Tayl...
KDD
2006
ACM
213views Data Mining» more  KDD 2006»
15 years 10 months ago
Learning sparse metrics via linear programming
Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficie...
Glenn Fung, Rómer Rosales
ICML
2007
IEEE
15 years 10 months ago
Discriminant kernel and regularization parameter learning via semidefinite programming
Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection o...
Jieping Ye, Jianhui Chen, Shuiwang Ji
ACSC
2005
IEEE
15 years 3 months ago
Integer Programming Models and Algorithms for Molecular Classification of Cancer from Microarray Data
Novel, high-throughput technologies are challenging the core of algorithmic methods available in Computer Science. Microarray technologies give Life Sciences researchers the oppor...
Regina Berretta, Alexandre Mendes, Pablo Moscato